Comparing and Contrasting Rough Set with Logistic Regression for a Dataset

This paper aims to discuss about the reasons behind the wide applicability of the rough set approach in real-life projects. The rough set-based approximations of (vague) concepts is one among the most central notions, available in the literature, for dealing with imperfect data and/or information. Moreover, as the approach based on rough sets is directly driven from data it turns out to be advantageous for real life projects where data plays a crucial role. Besides, using rough set approach one can deal efficiently with algorithmic issues, especially in the context of searching for relevant computational building blocks (granules) for approximation of complex vague concepts. In this paper, we would focus on these few aspects of rough sets, in order to explain its wide applicability in real-life projects.

Weather has an important role in people's lives, such as agriculture, economics, socio-economic, disaster management, and finance. So, weather prediction is very important to be considered. In the prediction process we are often faced with the problem of data incompleteness. Therefore, it needs a proper classification algorithm that able to handle incomplete attribute values in the training data. In this paper, we use two approaches to handle incomplete data, namely are rough set and fuzzy rough set based algorithms. To test the performance of the two algorithms, we use meteorological data to classify rain or dry season. Conclusion of the study showed that the rough set approach is more efficient than the fuzzy rough sets approach. The advantage of fuzzy rough set approach can predict all the conditions that may occur, which can't be done by the rough set approach.

This chapter presents a generic scheme for generating prediction rules based on rough set approach for stock market prediction. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data, which contains the minimal subset of attributes that are associated with a class label for prediction. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. For comparison, the results obtained using rough set approach were compared to that of artificial neural networks and decision trees. Empirical results illustrate that rough set approach achieves a higher overall prediction accuracy reaching over 97% and generates more compact and fewer rules than neural networks and decision tree algorithm.

Monotonic Variable Consistency Rough Set Approaches

Financial time series forecasting using rough sets with time-weighted rule voting

An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems

Rough sets theory (Pawlak 1980s) proved to be an excellent mathematical tool for the task of automated extraction of If –Then rule sets from table-organized data. In this paper, we employ an approach based on the relation of indiscernibility and Rough sets theory in comparison to the method based on pure classification. We have used a well-known ROSETTA software system. The main goal of this work is to compare rule set generated by ROSETTA and rule set generated by method based on pure classification. Comparison is conducted on real–life database from domain of medicine including recently discovered protein hormone Leptin.

Rough sets were introduced by Pawlak as a model to capture impreciseness in data and several techniques in different directions have been developed to perform operations on such data through the model. But the basic model of rough set introduced is unigranular from the granular computing point of view. So, attempts have been made to introduce multigranulation using rough sets. Two models called optimistic multigranular rough sets and pessimistic multigranular rough sets were introduce by Qian et al in 2006 and 2010 respectively. However, like the basic case, the information granules generated were introduced by equivalence relations and as a result the granules are members of partitions induced on the universe. However, such concepts have limited applications due to the rarity of equivalence relations or equivalently partitions of universes. The notion of cover is more general than partition and covers are available in abundance in real life situations. So, even in the case of basic rough sets the definition has been extended to develop covering based rough sets. Recently, the concept of multigranularity has been extended by Liu et al to introduce four types of covering based optimistic multigranular rough sets (CBOMGRS). In this paper we introduce the notion of Covering Based Pessimistic Multigranular Rough Sets (CBPMGRS) and proved some properties. The important observation is that some of the properties of basic rough sets, which were not true for CBOMGRS, are true for CBPMGRS. So, CBPMGRS seems to be more natural extensions of the basic concepts than CBOMGRS.

A rough set approach to intuitionistic fuzzy soft set based decision making

Rough set theory has been proposed by Pawlak in the early 80s to deal with inconsistency problems following from information granulation. It operates on an information table composed of a set U of objects described by a set Q of condition and decision attributes. Decision attributes make a partition of U into decision classes. Basic concepts of rough set theory are: indiscernibility relation on U, lower and upper (rough) approximations of decision classes, dependence and reduction of attributes from Q, and decision rules induced from rough approximations of decision classes. The original rough set idea was failing, however, to handle preferential ordering of domains of attributes (scales of criteria), as well as preferential ordering of decision classes. In order to deal with multiple criteria decision problems a number of methodological changes to the original rough set theory were necessary. The main change is the substitution of the indiscernibility relation by a dominance relation, which permits approximation of ordered sets. In multiple criteria decision context, the information table is composed of decision examples given by a decision maker. The Dominance-based Rough Set Approach (DRSA) applied to this information table results with a set of decision rules, being a preference model of the decision maker. It is more general than the classical multiple attribute utility model or outranking model, and it is more understandable because of its natural syntax. In this chapter, after recalling the classical rough set approach and DRSA, we review their fuzzy set extensions. Moreover, we characterize the dominance-based rough approximation of a fuzzy set, and we show that the classical rough approximation of a crisp set is its particular case. In this sense, DRSA is also relevant in the case where preferences are not considered, but just a kind of monotonicity relating values of different attributes is meaningful for the analysis of data at hand. In general terms, monotonicity concerns relationship between different aspects of a phenomenon described by data: for example, “the larger the house, the higher its price” or “the closer the house to the city centre, the higher its price”. In this perspective, DRSA gives a very general framework for reasoning about data using only monotonicity relationships.

In this paper we present a new approach to rough set approximations that permits to distinguish between two kinds of “imperfect” knowledge in a joint framework: on one hand, vagueness, due to imprecise knowledge and uncertainty typical of fuzzy sets, and on the other hand, ambiguity, due to granularity of knowledge originating from the coarseness typical of rough sets. The basic idea of our approach is that each concept is represented by an orthopair, that is, a pair of disjoint sets in the universe of knowledge. The first set in the pair contains all the objects that are considered as surely belonging to the concept, while the second set contains all the objects that surely do not belong to the concept. In this context, following some previous research conducted by us on the algebra of rough sets, we propose to define as rough approximation of the orthopair representing the considered concept another orthopair composed of lower approximations of the two sets in the first orthopair. We shall apply this idea to the classical rough set approach based on indiscernibility, as well as to the dominance-based rough set approach. We discuss also a variable precision rough approximation, and a fuzzy rough approximation of the orthopairs. Some didactic examples illustrate the proposed methodology.

Rough sets theory for multicriteria decision analysis

A hybrid approach of DEA, rough set and support vector machines for business failure prediction

The integrated methodology of rough set theory and artificial neural network for business failure prediction

Corporate memory (CM) is a major asset of any modern organization, and provides the company the special edge that makes it more competitive. Up to date, CM has not been applied to tourism, whose environmental change is rapid in both the supply environment and ecotourist behavior. In addition, the ecotourism often possesses numerous qualitative data. The qualitative nature of the data makes them difficult to analyze by standard statistical techniques. The rough set approach is one of the approaches that are suitable for processing qualitative information. The rough set theory offers a large collection of tools for knowledge discovery from data by computing the most relevant sets of attributes called reducts. In this paper, corporate memory in an organization uses the rough set as an approach to mine data due to the qualitative nature in ecotourism. A rough set based corporate memory is proposed to resolve the problems: (1) the local ecotourism association do not understand travelling purposes of tourists and how the tourists feedback to the place. (2) The local ecotourism association do not know how to improve the travel package such that the valued eco-tourists are losing. The proposed approach allows the local ecotourism association to understand tourist' behavior as a basis, which aims to achieve the goal of the sustainable ecotourism development.